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Interpreting supply chain dynamics: A quasi-chaos perspective

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  • Hwarng, H. Brian
  • Yuan, Xuchuan

Abstract

Chaotic phenomena, chaos amplification and other interesting nonlinear behaviors have been observed in supply chain systems. Chaos can be defined theoretically if the dynamics under study are produced only by deterministic factors. However, deterministic settings rarely present themselves in reality. In fact, real data are typically unknown. How can the chaos theory and its related methodology be applied in the real world? When the demand is stochastic, the interpretation and distribution of the Lyapunov exponents derived from the effective inventory at different supply chain levels are not similar to those under deterministic demand settings. Are the observed dynamics of the effective inventory random, chaotic, or simply quasi-chaos? In this study, we investigate a situation whereby the chaos analysis is applied to a time series as if its underlying structure, deterministic or stochastic, is unknown. The result shows clear distinction in chaos characterization between the two categories of demand process, deterministic vs. stochastic. It also highlights the complexity of the interplay between stochastic demand processes and nonlinear dynamics. Therefore, caution should be exercised in interpreting system dynamics when applying chaos analysis to a system of unknown underlying structure. By understanding this delicate interplay, decision makers have the better chance to tackle the problem correctly or more effectively at the demand end or the supply end.

Suggested Citation

  • Hwarng, H. Brian & Yuan, Xuchuan, 2014. "Interpreting supply chain dynamics: A quasi-chaos perspective," European Journal of Operational Research, Elsevier, vol. 233(3), pages 566-579.
    • Handle: RePEc:eee:ejores:v:233:y:2014:i:3:p:566-579
    DOI: 10.1016/j.ejor.2013.09.025
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    References listed on IDEAS

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    Cited by:

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    2. Pappas, Nikolaos & Papatheodorou, Andreas, 2017. "Tourism and the refugee crisis in Greece: Perceptions and decision-making of accommodation providers," Tourism Management, Elsevier, vol. 63(C), pages 31-41.
    3. Yuan, Xuchuan & Brian Hwarng, H., 2023. "Examining the dynamics of reactive capacity allocation through a chaos lens," European Journal of Operational Research, Elsevier, vol. 308(2), pages 912-928.
    4. Spiegler, Virginia L.M. & Naim, Mohamed M., 2017. "Investigating sustained oscillations in nonlinear production and inventory control models," European Journal of Operational Research, Elsevier, vol. 261(2), pages 572-583.
    5. Efthymios Katsoras & Patroklos Georgiadis, 2022. "A Dynamic Analysis for Mitigating Disaster Effects in Closed Loop Supply Chains," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    6. Zhao, LiuWei & Chang, Jianwei & DU, Jianguo, 2019. "Dynamics analysis on competition between manufacturing and remanufacturing in context of government subsidies," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 119-128.

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